An Operational Split-Window Algorithm for Generating Long-Term Land Surface Temperature Products From Chinese Fengyun-3 Series Satellite Data

Land surface temperature (LST) is an important parameter that characterizes the energy balance of the land surface, and it is widely used in various research fields. This article proposes an operational split-window (SW) algorithm for use with the Chinese Fengyun-3 (FY-3) series satellite data, with the purpose of generating long-term global LST products. The algorithm primarily involves three steps. First, the brightness temperatures of the FY-3 Visible and Infrared Radiometer (VIRR) were recalibrated using historical recalibration coefficients to improve the accuracy of the absolute radiometric calibration. Second, daily dynamic emissivity maps were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global emissivity dataset (GED) and vegetation/snow cover products based on the vegetation cover method. Finally, the coefficients of the SW algorithm were simulated using MODTRAN 5 combined with the SeeBor V5.0 atmospheric profile library and ASTER spectral library, and then, the coefficients were stratified by the view zenith angle (VZA) and atmospheric water vapor content (WVC) to improve the fitting accuracy. The proposed SW algorithm was integrated into the MUlti-source data SYnergized Quantitative (MUSYQ) remote sensing production system to then generate FY-3 VIRR LST products. Ten land surface sites from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER), Surface Radiation Budget (SURFRAD) networks, and nine water surface sites from the National Data Buoy Center (NDBC) were used to evaluate the accuracy of the FY-3 VIRR LST products. The results demonstrated that the accuracy of the historical recalibration coefficients of the FY-3A/B VIRR is higher than that of the operational calibration coefficients for LST retrieval. The evaluation results revealed that the FY-3A VIRR LST products (2009–2013) had a bias of 0.13 K and an RMSE of 2.77 K, and the FY-3B VIRR LST products (2011–2020) had a bias of −0.07 K and an RMSE of 2.83 K. These results demonstrate that the proposed operational SW algorithm has reasonable accuracy and can be used to produce global LST products from the FY-3 VIRR data.